Exemple #1
0
    def interpolate( self, in_lat, in_lon ):
        '''Interpolation method of wind at specified position.
        Uses bilinear aproximation between the vectors.
        Proportion is linear scaling of angle and linear scaling of length
        '''
        in_lat = norm_lat(in_lat)
        in_lon = norm_lon(in_lon)
        
        lat_ind = in_lat // self.step_la
        if in_lat == 90:
            lat_ind -= 1

        lon_ind = in_lon // self.step_lo
        if in_lon == 360:
            lon_ind = 0

        left = lon_ind * self.step_lo
        right = ( lon_ind + 1 ) * self.step_lo
        bottom = lat_ind * self.step_la
        top = ( lat_ind + 1 ) * self.step_la

        LB = self.wind_grid[bottom][left]
        LT = self.wind_grid[top][left]
        RB = self.wind_grid[bottom][norm_lon(right)]
        RT = self.wind_grid[top][norm_lon(right)]

        top_vec = self.proport( LT, RT, (in_lon - left) / (right - left) )
        bot_vec = self.proport( LB, RB, (in_lon - left) / (right - left) )

        return self.proport( bot_vec, top_vec, \
                             (in_lat - bottom) / (top - bottom) )
Exemple #2
0
def phi_theta( x, y, z ):
    '''Converts x, y, z tripplet into phi, theta normalizing output on the fly
    '''
    r = sqrt( x*x + y*y + z*z )
    phi = norm_lon(  atan2( y, x )*ratioPI  )
    theta = norm_lat( acos( z / r )*ratioPI - 90.0 )
    return ( phi, theta )
Exemple #3
0
    def interpolate_naive( self, in_lat, in_lon ):
        '''A bit stupid methon
        Resulting vector is proportional to how it`s close to \
        corner base grid vectors
        '''
        in_lat = norm_lat(in_lat)
        in_lon = norm_lon(in_lon)
        
        lat_ind = in_lat // self.step_la
        if in_lat == 90:
            lat_ind -= 1

        lon_ind = in_lon // self.step_lo
        if in_lon == 360:
            lon_ind = 0

        left = lon_ind * self.step_lo
        right = ( lon_ind + 1 ) * self.step_lo
        bottom = lat_ind * self.step_la
        top = ( lat_ind + 1 ) * self.step_la

        LB = self.wind_grid[bottom][left]
        LT = self.wind_grid[top][left]
        RB = self.wind_grid[bottom][norm_lon(right)]
        RT = self.wind_grid[top][norm_lon(right)]

        ltx = math.sqrt( \
            (in_lat-top)*(in_lat-top) + (in_lon-left)*(in_lon-left) )
        lbx = math.sqrt( \
            (in_lat-bottom)*(in_lat-bottom) + (in_lon-left)*(in_lon-left) )
        rtx = math.sqrt( \
            (in_lat-top)*(in_lat-top) + (in_lon-right)*(in_lon-right) )
        rbx = math.sqrt( \
            (in_lat-bottom)*(in_lat-bottom) + (in_lon-right)*(in_lon-right) )
        norma = ltx + lbx + rtx + rbx

        return ( ((norma-ltx)*LT[0]/norma + (norma-lbx)*LB[0]/norma + \
                 (norma-rtx)*RT[0]/norma + (norma-rbx)*RB[0]/norma)/3 , \
                 ((norma-ltx)*LT[1]/norma + (norma-lbx)*LB[1]/norma + \
                 (norma-rtx)*RT[1]/norma + (norma-rbx)*RB[1]/norma)/3 )
Exemple #4
0
    def points_gen( self, psi, units=DEGREES, verbose = True ):
        '''Genertion of the points randomly distributed with *average*
        resolution of psi.

        Arguments:
        psi (float) -- resolution
        units (DEGREES/RADIANS) -- how psi is set (default: DEGREES)

        '''
        k = 0.5 / pi

        p0 = k * self.domain.phi[0]
        p1 = k * self.domain.phi[1]

        t0 = 0.5* ( 1 + cos( self.domain.theta[1] ) )
        t1 = 0.5* ( 1 + cos( self.domain.theta[0] ) )

        N = self.domain.point_count( psi, units )
        if verbose:
            print("points amount to generate " + str(N))
        self.points_clear()
        # arccos method:
        for j in range( N ):
            
            U = random.uniform( p0, p1 )
            V = random.uniform( t0, t1 )

            phi = 2*pi* U
            theta = acos( 2*V - 1 )

            x,y,z = geocoords.xyz_spherical( theta, phi )

            self.points.append( (x,y,z) )
            self.points_angular.append( ( norm_lon( phi*ratioPI), \
                                          norm_lat( theta*ratioPI) ) )

        return